Large Datasets Research Articles

HindiSumm: A Hindi Abstractive Summarization benchmark dataset

Abstractive Text Summarization (ATS) is a task to create a novel summary by generating fresh sentences incorporating new words or rephrasing the article. It is a complex task as the model needs to understand the semantic similarity between the sentences of the text. To fulfill this, there is a need for a large annotated benchmark dataset, which is available for resource-rich languages such as English and non-indic languages. In contrast, for the less-resourced languages, such as Indic languages, the available datasets are limited and involve very short summary sentences. Hence, a language-specific abstractive summarization dataset called HindiSumm was introduced for Hindi, consisting of 570,000 text-summary pairs from Navbharat Times across 21 domains. The HindiSumm dataset’s efficiency is evaluated extrinsically and intrinsically by using various metrics. Furthermore, two recent multilingual-cased pre-trained models are fine-tuned on the HindiSumm dataset individually. In addition, an ensembled approach using weighted averaging is also incorporated to check the efficacy of the proposed dataset. The model is tested with the in-house created dataset, and results are evaluated on ROUGE scores and show significant improvements of around 13.2% for the proposed HindiSumm compared to other benchmark datasets. In the future, the HindiSumm dataset will promote the progress of ATS for the Indian language.

Zero-shot prompt-based video encoder for surgical gesture recognition.

In order to produce a surgical gesture recognition system that can support a wide variety of procedures, either a very large annotated dataset must be acquired, or fitted models must generalize to new labels (so-called zero-shot capability). In this paper we investigate the feasibility of latter option. Leveraging the bridge-prompt framework, we prompt-tune a pre-trained vision-text model (CLIP) for gesture recognition in surgical videos. This can utilize extensive outside video data such as text, but also make use of label meta-data and weakly supervised contrastive losses. Our experiments show that prompt-based video encoder outperforms standard encoders in surgical gesture recognition tasks. Notably, it displays strong performance in zero-shot scenarios, where gestures/tasks that were not provided during the encoder training phase are included in the prediction phase. Additionally, we measure the benefit of inclusion text descriptions in the feature extractor training schema. Bridge-prompt and similar pre-trained+prompt-tuned video encoder models present significant visual representation for surgical robotics, especially in gesture recognition tasks. Given the diverse range of surgical tasks (gestures), the ability of these models to zero-shot transfer without the need for any task (gesture) specific retraining makes them invaluable.

DONGLE 2.0: Direct FPGA-Orchestrated NVMe Storage for HLS

Rapid growth in data size poses significant computational and memory challenges to data processing. FPGA accelerators and near-storage processing have emerged as compelling solutions for tackling the growing computational and memory requirements. Many FPGA-based accelerators have shown to be effective in processing large data sets by leveraging the storage capability of either host-attached or FPGA-attached storage devices. However, the current HLS development environment does not allow direct access to host-or FPGA-attached NVMe storage from the HLS code. As such, users must frequently hand off between HLS and host code to access data in storage, and such a process requires tedious programming to ensure functional correctness. Moreover, since the HLS code uses radically different methods to access storage compared to DRAM, the HLS codebase targeting DRAM-based platforms cannot be easily ported to NVMe-based platforms, resulting in limited code portability and reusability. Furthermore, frequent suspension of HLS kernel and synchronization between CPU and FPGA introduce significant latency overhead and require sophisticated scheduling mechanisms to hide latency. To address these challenges, we propose a new HLS storage interface named DONGLE 2.0 that enables direct FPGA-orchestrated NVMe storage access. By providing a unified interface for storage and memory access, DONGLE 2.0 allows a single-source HLS program to target multiple memory/storage devices, thus making the codebase cleaner, portable, and more efficient. DONGLE 2.0 is an extension to DONGLE 1.0 [ 1 ] but adds support for host-attached storage. While its primary focus is still on FPGA NVMe access in near-storage configurations, the added host storage support ensures its compatibility with platforms that lack native support for FPGA-attached NVMe storage. We implemented a prototype of DONGLE 2.0 using an AMD/Xilinx Alveo U200 FPGA and Solidigm DC-P4610 SSD. Our evaluation on various workloads showed a geometric mean speed-up of 2.3× and a reduction in lines of code (LoC) by 2.4× compared to the state-of-the-art commercial platform when using FPGA-attached NVMe storage. Moreover, DONGLE 2.0 demonstrated a geometric mean speed-up of 1.5× and a reduction in LoC by 2.4× compared to the state-of-the-art commercial platform when using host-attached NVMe storage.

MolPipeline: A Python Package for Processing Molecules with RDKit in Scikit-learn.

The open-source package scikit-learn provides various machine learning algorithms and data processing tools, including the Pipeline class, which allows users to prepend custom data transformation steps to the machine learning model. We introduce the MolPipeline package, which extends this concept to cheminformatics by wrapping standard RDKit functionality, such as reading and writing SMILES strings or calculating molecular descriptors from a molecule object. We aimed to build an easy-to-use Python package to create completely automated end-to-end pipelines that scale to large data sets. Particular emphasis was put on handling erroneous instances, where resolution would require manual intervention in default pipelines. MolPipeline provides the building blocks to enable seamless integration of common cheminformatics tasks within scikit-learn's pipeline framework, such as scaffold splits and molecular standardization, making pipeline building easily adaptable to diverse project requirements.

The interplay between mobilome and resistome in Staphylococcus aureus.

Antibiotic resistance genes (ARGs) in Staphylococcus aureus can disseminate vertically through successful clones, but also horizontally through the transfer of genes conveyed by mobile genetic elements (MGEs). Even though underexplored, MGE/ARG associations in S. aureus favor the emergence of multidrug-resistant clones, which are challenging therapeutic success in both human and animal health. This study investigated the interplay between the mobilome and the resistome of more than 10,000 S. aureus genomes from human and animal origin. The analysis revealed a remarkable diversity of MGEs and ARGs, with plasmids and transposons being the main carriers of ARGs. Numerous MGE/ARG associations were identified, suggesting that MGEs play a critical role in the dissemination of resistance. A high degree of similarity was observed in MGE/ARG associations between human and animal isolates, highlighting the potential for unrestricted spread of ARGs between hosts. Our results showed that in parallel to clonal expansion, MGEs and their associated ARGs can spread across different strain types sequence types (STs), favoring the evolution of these clones and their adaptation in selective environments. The high variability of MGE/ARG associations within individual STs and their spread across several STs highlight the crucial role of MGEs in shaping the S. aureus resistome. Overall, this study provides valuable insights into the complex interplay between MGEs and ARGs in S. aureus, emphasizing the need to elucidate the mechanisms governing the epidemic success of MGEs, particularly those implicated in ARG transfer.IMPORTANCEThe research presented in this article highlights the importance of understanding the interactions between mobile genetic elements (MGEs) and antibiotic resistance genes (ARGs) carried by Staphylococcus aureus, a versatile bacterium that can be both a harmless commensal and a dangerous pathogen for humans and animals. S. aureus has a great capacity to acquire and disseminate ARGs, enabling efficient adaption to various environmental or clinical conditions. By analyzing a large data set of S. aureus genomes, we highlighted the substantial role of MGEs, particularly plasmids and transposons, in disseminating ARGs within and between S. aureus populations, bypassing host barriers. Given that multidrug-resistant S. aureus strains are classified as a high-priority pathogen by global health organizations, this knowledge is crucial for understanding the complex dynamics of transmission of antibiotic resistance in this species.

Deep learning for surgical workflow analysis: a survey of progresses, limitations, and trends

Automatic surgical workflow analysis, which aims to recognize the ongoing surgical events in videos, is fundamental for developing context-aware computer-assisted systems. This paper reviews representative surgical workflow recognition algorithms based on deep learning, outlining their merits, limitations, and future research directions. The literature survey was performed on three large bibliographic databases, covering 67 lary sources, which were comparatively analyzed in terms of spatial feature modeling, spatio-temporal feature modeling, input pre-processing, regularization and post-processing algorithms, as well as learning strategies. Then, common public datasets and evaluation metrics for surgical workflow recognition are also described in detail. Finally, we discuss all literature from different perspectives, and point out the challenges, possible solutions and future trends. The need for more diverse and larger datasets, the potential of unsupervised and semi-supervised learning approaches, comprehensive and equitable metrics, establishing complete regulatory and data standards, and interoperability will be key challenges in translating models to clinical operating rooms. And we propose that surgical activity anticipation and employing large language model as training assistant are interesting research directions in surgical workflow analysis.

Coping With Interstitial Cystitis/Bladder Pain Syndrome.

Compensatory coping, or maladaptive alterations in behavior with the intention of preventing or managing symptoms, is increasingly being explored as a key factor in how people respond to bladder conditions. Preliminary investigations have identified relations between coping behaviors and psychological distress in urologic conditions, including interstitial cystitis/bladder pain syndrome (IC/BPS). However, previous explorations of coping have not accounted for heterogeneity in coping behaviors or addressed the likelihood that some coping behaviors may be more adaptive than others. This study sought to examine how two specific types of coping behaviors, primary control coping and disengaged coping, are related to distress and symptoms in IC/BPS, and to explore the potential role of pain phenotype in this relationship. A secondary data analysis was conducted with a large community data set (N = 677 women with IC/BPS) and employed descriptive and inferential statistics to characterize coping patterns and explore novel predictors of distress. Results indicated that almost all participants engaged in at least one compensatory coping behavior within the last week. Both types of coping behaviors correlated with psychological symptoms, and when controlling for relevant clinical variables (i.e., age and severity of urinary symptoms), disengaged coping behaviors were significantly associated with psychological distress. Further, the addition of pain phenotype to multiple regression models resulted in a more effective predictive model when considering the relation between coping behaviors and depression. By investigating more deeply the relationship between coping and distress, understanding of potential risk factors and mechanisms is increased, offering valuable insights for intervention strategies for IC/BPS patients.

Investigation of ComBat Harmonization on Radiomic and Deep Features from Multi-Center Abdominal MRI Data.

ComBat harmonization has been developed to remove non-biological variations for data in multi-center research applying artificial intelligence (AI). We investigated the effectiveness of ComBat harmonization on radiomic and deep features extracted from large, multi-center abdominal MRI data. A retrospective study was conducted on T2-weighted (T2W) abdominal MRI data retrieved from individual patients with suspected or known chronic liver disease at three study sites. MRI data were acquired using systems from three manufacturers and two field strengths. Radiomic features and deep features were extracted using the PyRadiomics pipeline and a Swin Transformer. ComBat was used to harmonize radiomic and deep features across different manufacturers and field strengths. Student's t-test, ANOVA test, and Cohen's F score were applied to assess the difference in individual features before and after ComBat harmonization. Between two field strengths, 76.7%, 52.9%, and 26.7% of radiomic features, and 89.0%, 56.5%, and 0.1% of deep features from three manufacturers were significantly different. Among the three manufacturers, 90.1% and 75.0% of radiomic features and 89.3% and 84.1% of deep features from two field strengths were significantly different. After ComBat harmonization, there were no significant differences in radiomic and deep features among manufacturers or field strengths based on t-tests or ANOVA tests. Reduced Cohen's F scores were consistently observed after ComBat harmonization. ComBat harmonization effectively harmonizes radiomic and deep features by removing the non-biological variations due to system manufacturers and/or field strengths in large multi-center clinical abdominal MRI datasets.

Big data applications: overview, challenges and future

Big Data (i.e., social big data, vehicular big data, healthcare big data etc) points to massive and complex data, that require special technologies and approaches for storage, processing, and analysis. Similarly, big data applications are software and systems utilizing large and complex datasets to extract insights, support decision-making, and address diverse business and societal challenges. Recently, the significance of big data applications has grown immensely for organizations across diverse sectors as they increasingly rely on insights derived from data. The increasing reliance on data insights has rendered traditional technologies and platforms inefficient due to scalability limitations and performance issues. This study contributes by identifying key domains impacted by big data, examining its effect on decision-making, addressing inherent complexities and opportunities, exploring core technologies, and offering solutions for potential concerns. Additionally, it conducts a comparative analysis to demonstrate the superiority of this research. These contributions provide valuable insights into the evolving landscape shaped by big data applications.

Foundational model aided automatic high-throughput drug screening using self-controlled cohort study.

Developing medicine from scratch to governmental authorization and detecting adverse drug reactions (ADR) have barely been economical, expeditious, and risk-averse investments. The availability of large-scale observational healthcare databases and the popularity of large language models offer an unparalleled opportunity to enable automatic high-throughput drug screening for both repurposing and pharmacovigilance. To demonstrate a general workflow for automatic high-throughput drug screening with the following advantages: (i) the association of various exposure on diseases can be estimated; (ii) both repurposing and pharmacovigilance are integrated; (iii) accurate exposure length for each prescription is parsed from clinical texts; (iv) intrinsic relationship between drugs and diseases are removed jointly by bioinformatic mapping and large language model - ChatGPT; (v) causal-wise interpretations for incidence rate contrasts are provided. Using a self-controlled cohort study design where subjects serve as their own control group, we tested the intention-to-treat association between medications on the incidence of diseases. Exposure length for each prescription is determined by parsing common dosages in English free text into a structured format. Exposure period starts from initial prescription to treatment discontinuation. A same exposure length preceding initial treatment is the control period. Clinical outcomes and categories are identified using existing phenotyping algorithms. Incident rate ratios (IRR) are tested using uniformly most powerful (UMP) unbiased tests. We assessed 3,444 medications on 276 diseases on 6,613,198 patients from the Clinical Practice Research Datalink (CPRD), an UK primary care electronic health records (EHR) spanning from 1987 to 2018. Due to the built-in selection bias of self-controlled cohort studies, ingredients-disease pairs confounded by deterministic medical relationships are removed by existing map from RxNorm and nonexistent maps by calling ChatGPT. A total of 16,901 drug-disease pairs reveals significant risk reduction, which can be considered as candidates for repurposing, while a total of 11,089 pairs showed significant risk increase, where drug safety might be of a concern instead. This work developed a data-driven, nonparametric, hypothesis generating, and automatic high-throughput workflow, which reveals the potential of natural language processing in pharmacoepidemiology. We demonstrate the paradigm to a large observational health dataset to help discover potential novel therapies and adverse drug effects. The framework of this study can be extended to other observational medical databases.

Inequalities in global residential cooling energy use to 2050

Intersecting socio-demographic transformations and warming climates portend increasing worldwide heat exposures and health sequelae. Cooling adaptation via air conditioning (AC) is effective, but energy-intensive and constrained by household-level differences in income and adaptive capacity. Using statistical models trained on a large multi-country household survey dataset (n = 673,215), we project AC adoption and energy use to mid-century at fine spatial resolution worldwide. Globally, the share of households with residential AC could grow from 27% to 41% (range of scenarios assessed: 33-48%), implying up to a doubling of residential cooling electricity consumption, from 1220 to 1940 (scenarios range: 1590-2377) terawatt-hours yr.−1, emitting between 590 and 1,365 million tons of carbon dioxide equivalent (MtCO2e). AC access and utilization will remain highly unequal within and across countries and income groups, with significant regressive impacts. Up to 4 billion people may lack air-conditioning in 2050. Our global gridded projections facilitate incorporation of AC’s vulnerability, health, and decarbonization effects into integrated assessments of climate change.

A deep learning approach for white blood cells image generation and classification using SRGAN and VGG19

The classification of White Blood Cells (WBCs) is crucial for diagnosing diseases, monitoring treatment effectiveness, and understanding how the immune system functions. In this paper, we propose a deep learning approach to classify WBCs using Super Resolution Generative Adversarial Network (SRGAN) and Visual Geometry Group 19 (VGG19). Firstly, microscopic images of WBCs are generated using the SRGAN to obtain more precise and high-resolution images, which are then classified with a pretrained VGG19 classifier. Low-resolution (LR) images are inputted into the generator of SRGAN, and its discriminator compares the High-resolution (HR) image with LR, generating super-resolution images to minimize misclassification risks. A large dataset of 12,447 images containing four classes of WBCs (Eosinophil, Lymphocyte, Monocyte, and Neutrophil) is utilized to train and validate our proposed model. Following extensive experimental analysis, our proposed model achieves a test accuracy of 94.87 %, surpassing traditional Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Hybrid CNN-RNN models, and other conventional approaches. The generated images of SRGAN overcome challenges associated with misclassification due to the poor resolution of microscopic images, while the use of a pretrained model as a classifier reduces classification complexity. The source code of the entire work is available at https://github.com/Jannatul-Ferdousi/SRGAN_VGG19_WBC.git.

Waste management and water quality evaluation prediction in urban environments through advanced robust hybrid machine learning algorithms

Water quality management is a crucial aspect of environmental protection, requiring the monitoring and regulation of effluent discharges into surface water bodies. This research introduces a novel approach to predicting Water Quality Evaluation (WQE) through a unique hybrid model, ABC-DWKNN-ICA, which integrates the Distance-weighted K-Nearest Neighbors (DWKNN) algorithm with the Artificial Bee Colony (ABC), Firefly Algorithm (FA), Imperialist Competitive Algorithm (ICA), and Gravitational Search Algorithm (GSA). Utilizing a comprehensive dataset of 1106 data points from Telangana, India, spanning 2018–2020, the study examines a range of water quality parameters, including Ground Water Level (GWL), Potential of Hydrogen (PH), Electrical Conductivity (EC), and others. The ABC-DWKNN-ICA model demonstrates exceptional performance in terms of Recall, Precision, Accuracy, and F1 Score for WQE prediction, distinguishing itself with enhanced feature selection, improved classification accuracy, robustness to noise and outliers, reduced dimensionality, and scalability to large datasets. This hybrid model represents a significant advancement over existing approaches, including traditional Hybrid Machine Learning (HML) algorithms such as ABC-DWKNN, FA-DWKNN, ICA-DWKNN, and GSA-DWKNN. By focusing on the capabilities of ABC-DWKNN-ICA rather than comparing all HML algorithms, the research highlights its superior effectiveness in water quality prediction, with performance metrics of 0.83 for Recall, 0.86 for Precision, 0.91 for Accuracy, and 0.86 for F1 Score. This study thus fills a critical research gap by demonstrating the model's value in environmental data analysis and offering promising prospects for more effective management of water resources. Additionally, feature selection, dimensionality reduction, enhanced accuracy, noise handling, and imbalanced dataset management are key advantages of the proposed model.

Neural network reconstruction of the left atrium using sparse catheter paths.

Catheter-based radiofrequency ablation for pulmonary vein isolation has become the first line of treatment for atrial fibrillation in recent years. This requires a rather accurate map of the left atrial sub-endocardial surface including the ostia of the pulmonary veins, which requires dense sampling of the surface and currently takes more than 10min. The focus of this work is to provide left atrial visualization early in the procedure to ease procedure complexity and enable further workflows, such as using catheters that have difficulty sampling the surface. We propose a dense encoder-decoder network with a novel regularization term to reconstruct the shape of the left atrium from partial data which is derived from simple catheter maneuvers. To train the network, we acquire a large dataset of 3D atria shapes and generate corresponding catheter trajectories, from which traversed point clouds are obtained. Once trained, we show that the suggested network can sufficiently approximate the atrium shape based on a given trajectory. We compare several network solutions for the 3D atrium reconstruction. We demonstrate that the solution proposed produces realistic visualization using partial acquisition within a 3-min time interval using human clinical cases.

Discordance between peritumoral and subareolar injections for mapping sentinel lymph nodes in the breast.

Sentinel node biopsy (SNB) is a common staging tool for breast cancer. Initially, peritumoral (PT) injections were used, however subareolar (SA) injections were later introduced to simplify the technique. Controversy remains regarding whether PT and SA injections map the same sentinel lymph nodes (SLNs). This study aimed to determine whether the regional location of breast SLNs differs when using PT versus SA injections using a large dataset from a single institution. A total of 1035 patients who underwent breast SNB (PT injections: n = 858 and SA injections: n = 177) with lymphoscintigraphy and SPECT/CT were included. The identified SLN locations using SA injections were compared with those using PT injections. Differences in drainage proportions and odds ratios (ORs) for each clockface breast region and the whole breast were calculated using a two-proportion z-test and Fisher's Exact Test. A higher proportion of internal mammary SLNs were identified using PT injections for the whole breast (0.30 versus 0.09) and for all breast regions, with all regions showing statistical significance except the upper outer quadrant. Similarly, ORs showed identification of internal mammary SLNs was significantly higher when using PT injections (4.35, 95% CI 2.53 to 7.95). There were no significant differences in identifying axillary SLNs between injection sites. This is the largest cohort study to compare the regional location of breast SLNs identified using PT injections versus SA injections. Discordance was shown in the SLNs identified between injection techniques, with PT injections more frequently identifying internal mammary SLNs.

Optimizing Capsule Network Performance with Enhanced Squash Function for Classification Large-Scale Bone Marrow Cells

Capsule networks (CapsNet) have emerged as a promising architectural framework for various machine-learning tasks and offer advantages in capturing hierarchical relationships and spatial hierarchies within data. One of the most crucial components of CapsNet is the squash function, which plays a pivotal role in transforming capsule activations. Despite the success achieved by standard squash functions, some limitations remain. The difficulty learning complex patterns with small vectors and vanishing gradients are major limitations. Standard squash functions may struggle to handle large datasets. We improve our methodology to enhance squash functions to address these challenges and build on our previous research, which recommended enhancing squash functions for future improvements. Thus, high-dimensional, and complex data scenarios improve CapsNet’s performance. Enhancing CapsNet for complex tasks like bone marrow (BM) cell classification requires optimizing its fundamental operations. Additionally, the squash function affects feature representation and routing dynamics. Additionally, this enhancement improves feature representation, preserves spatial relationships, and reduces routing information loss. The proposed method increased BM data classification accuracy from 96.99% to 98.52%. This shows that our method improves CapsNet performance, especially in complex and large-scale tasks like BM cells. Comparing the improved CapsNet model to the standard CapsNet across datasets supports the results. The enhanced squash CapsNet outperforms the standard model on MNIST, CIFAR-10, and Fashion MNIST with an accuracy of 99.83%, 73%, and 94.66%, respectively. These findings show that the enhanced squash function improves CapsNet performance across diverse datasets, confirms its potential for real-world machine learning applications, and highlight the necessity for additional research.

Advances in Artificial Intelligence-aided Intraoral Imaging Analysis in Periodontics

Artificial intelligence has increasingly influenced the field of periodontology by enhancing diagnostic accuracy and treatment planning through advanced data-driven techniques. It was aimed to examine the integration of artificial intelligence, particularly deep learning and machine learning, in analyzing intraoral photographs for periodontal conditions in this review. Periodontal assessments rely on clinical and radiographic evaluations, but artificial intelligence introduces a transformative approach by analyzing large datasets to improve clinical decision-making. The review investigates the effectiveness of artificial intelligence-enhanced intraoral photograph analysis, focusing on methodologies for dataset creation, model development, training, and performance evaluation. A thorough search of databases such as PubMed, Scopus, Google Scholar, and IEEE Xplore identified 338 articles, with 16 meeting the inclusion criteria. These studies primarily utilized convolutional neural networks and architectures like DeepLabv3+ and U-Net, demonstrating high accuracy in detecting conditions such as gingivitis, dental plaque, and other periodontal issues. The dataset sizes ranged from 110 to 7220 images, affecting the models' generalizability. Most studies employed supervised learning, with models trained on labeled datasets to achieve precise diagnostic outcomes. The review highlights that while artificial intelligence and machine learning techniques, including convolutional neural networks and U-Net, offer significant improvements in periodontal diagnostics, the choice of model and the quality of the dataset are crucial for performance. Hybrid approaches that combine automated and expert-driven methods might provide a balance between efficiency and accuracy. The successful integration of artificial intelligence into clinical practice requires continuous validation and adaptation to ensure that these technologies remain accurate and relevant. Future research should focus on enhancing model robustness, expanding dataset diversity, and refining clinical applications to fully exploit the potential of artificial intelligence in periodontology.

A research paper on how Artificial Intelligence changes the Human Resource Activities inside the organization and provides the significant improvement in workforce working environment for various day to day decisions making along with their efficiency and their productivity

This paper speaks volumes about the various processes of Human resource like recruitment , training, selections, performance and development and workforce planning and how the Artificial Intelligence has revolutionized over the period of time by providing various insights and supporting in the decision making process. Artificial intelligence has provided a bias free and advanced algorithms due to which the screening and scheduling of HR process has been done with accurate parameters and lightning fast speed. This study provides a detailed insight to the AI intervention and its effectiveness in providing a great decision making system to the Human resource professionals. This platform is utilizing the sentiment analysis and catboats to monitor and improve the work place environment, which pushes the positivity in work culture and boosts productivity The AI aids in providing continuous feedback, various goal settings and tracking the performance productivity. This also provides a gist as well as in depth knowledge of top performers of the unit and also share the areas of improvement by which the others can reach up to a new bench mark. This AI has been a boon to the HR individuals thoroughly, especially during the performance appraisals. On the other AI enables personalized training programs and adaptive learning paths, addressing skill gaps and aligning employee development along with organizational goals.The point to ponder over is that the AI has also played a crucial role in workforce planning and analytics, additionally, it has aided by providing tools to forecast future workforce needs, optimize resource allocation, and enhance strategic decision-making. This process which is done by IA is by analyzing large datasets, AI also predicts turnover trends and informs succession planning, contributing to organizational productive and efficient life cycle The research has infused a mixed-methods approach, which combines qualitative and quantitative data collection through surveys, interviews, and case studies, alongside statistical analysis of HR metrics pre- and post-AI implementation. The basics of this research paper aims to provide valuable insights for HR professionals and organizational leaders on leveraging AI to enhance HR effectiveness and drive business success. There a strong determination which ensures that the findings will contribute to the evolving body of knowledge on AI in HR and support the development of strategic frameworks for AI adoption in human resource management on a longer run.

Multi-cameras calibration System Based Deep Learning Approach and Beyond: A Survey

The process of determining camera settings to deduce geometric attributes from recorded sequences is known as camera calibration. This process is essential in the fields of robotics and computer vision, encompassing both two-dimensional and three-dimensional applications. Traditional calibration methods, however, are time-consuming and require specific expertise. Recent endeavors have demonstrated that learning-based systems can replace the monotonous tasks associated with manual calibrations. Responses have been examined through a range of learning techniques, networks, geometric assumptions, and datasets. A thorough examination of camera calibration systems that rely on learning algorithms is offered in this paper, assessing their advantages and disadvantages. The primary categories of calibration presented are the regular pinhole camera model, distortion camera model, cross-sensor model, and cross-view model. These categories align with current research trends and have diverse applications. As there is no existing standard in this field, a large dataset of calibration has been created, which can be used as a public platform to assess the effectiveness of current methods. This collection consists of both artificially generated and genuine data, including images and videos obtained from various cameras in different locations. The difficulties faced will be analyzed, and alternative avenues for further research will be suggested in the next stage of this project. This survey represents the initial attempt to perform camera calibration using learning-based methods spanning a period of eight years. Our findings indicate that learning-based methods significantly reduce the time and expertise required for calibration while maintaining or improving accuracy compared to traditional methods. Specifically, our research demonstrates a calibration error reduction of up to 20% and speed improvements by a factor of three compared to traditional methods, as well as better adaptability to different camera types and environments.

PSLBI-22 Identifying plasma metabolites influencing body weight in Salmonella challenged growing pigs: A machine learning approach

Abstract Body weight (BW) is an important component of pig productivity. Understanding the metabolic factors affecting growth rate is key to guiding nutritional strategies and bringing economic benefits to pig farms. Machine learning (ML) algorithms have been used to capture patterns in large datasets and predict animal production traits based on integrated information of performance data and blood records. However, utilizing ML models and metabolomic profiling to predict BW of pigs in small datasets with challenged animals has yet to be reported. Therefore, this study investigated use of plasma metabolites in predicting BW of pigs at the growing stage. This simulation used data from gilts (n = 40; BW = 25.6 ± 2.3 kg) raised over a 28-d trial. Plasma samples and BW were collected on d 0, 14, and 28 after 8 h of fasting. Ninety plasma metabolites were identified and quantified from an untargeted metabolomics assay. The development dataset included 30, and the validation dataset contained 10 unique animals with repeated measures in time. The metabolites were log-transformed, Pareto-scaled and missing values were imputed using the minimum nonzero value method. The BW prediction model was performed in R with the caret package. The optimal hyper-parameter configuration for the random forest and neural network algorithms was evaluated based on repeated cross-validation. The final model was the random forest composed of 400 trees with 45 randomly selected predictors. Model accuracy was estimated on repeated cross-validation with 10-folds and 5 repeats and compared with the validation set based on the coefficient of determination (R2), the root mean squared error (RMSE), and Pearson correlation (r). The metabolites with greater contribution with variable importance score above 10 to the model development were extracted. Aiming to understand differences in BW categories, the model response was visualized with an alluvial diagram for the 3 variables with the greatest metabolite importance, and BW was classified as high, medium, and low. The main metabolites driving BW were lysine, oleic acid, aminomalonic acid, myo-inositol, and linoleic acid. The RMSE after cross-validation was 7.22 kg of BW (r = 0.70), and R2 = 0.39. For pigs with a high BW, the predicted BW was explained by generally less plasmatic concentrations of lysine, oleic acid, and aminomalonic acid. Lysine is the main amino acid for muscle protein accretion, whereas oleic acid is for energy substrate. Aminomalonic acid is a metabolite resulting from glycine metabolism and is important in reversing oxidative damage caused by pathogens. Therefore, decreased plasma lysine, amino malonic acid, and oleic acid may indicate faster growth and lean deposition, leading to heavier BW in pigs. Using random forests helped to connect the plasma dynamics to BW and is a promising avenue to understand the main drivers in BW of group-housed pigs.